An active foot lifter orthosis based on a PCPG algorithm
Matthieu Duvinage, René Jiménez-Fabián, Thierry Castermans, Olivier Verlinden and Thierry Dutoit
Abstract— Central pattern generators (CPGs) are known to
play an important role in the generation of rhythmic movements
in gait, both in animals and humans. The comprehension of
their underlying mechanism has led to the development of
an important family of algorithms at the basis of autonomous
walking robots. Recently, it has been shown that human gait
could be modeled using a subclass of those algorithms, namely
a Programmable Central Pattern Generator (PCPG).
In this paper, we present a foot lifter orthosis driven by this
algorithm. After a learning phase, the PCPG is able to generate
adequate rhythmic gait patterns both for constant speeds and
acceleration phases. Its output is used to drive the orthosis
actuator during the swing phase, in order to help patients
suffering from foot drop (the orthosis just follows the movement
during the stance phase). The most interesting property of this
algorithm is the possibility to generate a smooth output signal
even during speed transitions. In practice, given that human
gait is not perfectly periodic, the phase of this signal needs to
be reset with actual movement. Therefore, two phase-resetting
procedures were studied: one standard hard phase-resetting
leading to discontinuities and one original soft phase-resetting
allowing to recover the correct phase in a smooth way. The
simulation results and complete design of the orthosis hardware
and software are presented.
I. I NTRODUCTION
Accidents, wars or diseases are the main reasons for
loosing the total control of a leg. For ages, several types
of leg prostheses/orthoses have been developed in order
to help disabled people and enhance their daily life. The
main objective of these prostheses/orthoses is to allow their
user to walk as naturally as possible considering the huge
complexity of human gait. Active systems have recently
become available on the market. Contrary to standard
passive systems, these are equipped with self-propulsion
capabilities leading to an enhanced locomotion speed, much
less fatigue and overall a better comfort of the patient.
Two main categories of active prostheses exist to date. In
the first category, a sensing system analyzes the motion of a
specific part of the patient’s body (e.g. the healthy leg, the
upper-body or the hip). Using this kinematics information,
the control system identifies the phase of the gait cycle and
then adequately triggers an actuator [1]–[5]. The movement
of the second type of active prostheses (or orthoses) is
guided by myoelectric signals recorded at the surface of the
skin, just above the muscles involved in gait [6]–[8].
Matthieu Duvinage (FNRS Research Fellow), Thierry Castermans and
Thierry Dutoit are with Faculty of Electrical Engineering, TCTS Lab,
University of Mons, 7000 Mons, Belgium
René Jiménez-Fabián and Olivier Verlinden are with Faculty of Mechanical Engineering, MRDV lab, University of Mons, 7000 Mons, Belgium
Matthieu
Duvinage
is
the
corresponding
author:
Matthieu.Duvinage@umons.ac.be
Although active prosthetic technology really offers
important improvements, the non-natural gait problems
of the patients are only partially solved. Indeed, active
prosthetic systems seldom adapt to the specificities of
the patient’s gait and, more specifically, to variations of
his walking dynamics at constant or varying speeds. This
leads to non energy efficient systems and oblige patients to
compensate for these imperfections while walking.
In this general context, we propose to develop an active
prosthesis/orthosis system which is inspired by real walking
mechanism. Actually, human locomotion is known to be
based on a very complex hierarchical system which includes
several control networks located at spinal and supraspinal
levels [9]. In broad outline, high-level commands are
sent by the brain to Central Pattern Generators (CPGs),
composed of interneurons and motoneurons located in the
spinal cord, which generate periodic gait patterns at the
desired speed. This basic principle has widely inspired the
field of robotics, particularly in the development of small
autonomous walking robots, from multi-legged insect-like
robots to humanoids [10].
One of the algorithms developed in this framework is called
Programmable Central Pattern Generator (PCPG) [11].
Basically, a PCPG is able to learn and subsequently
reproduce any given rhythmic gait pattern. The interest of
this particular CPG is twofold: firstly, the parametrization
learning is simple and secondly, the learned parameters
(i.e. the pattern magnitude and frequency) may be adjusted
in order to generate different speeds. During these speed
changes, the output signals sent to the walking robot
actuators present smooth transitions, which is particularly
important for practical applications.
The aim of this paper is to expose the principles and a
proof of concept of integrating a human gait model in a foot
lifter orthosis using such PCPG algorithm. Noticing that foot
kinematics is known to be the most complex signal to model
in human locomotion, this work can be easily extended to
full prostheses in its principles. In Section 2, the bases of
Programmable Central Pattern Generators to learn a gait
pattern are detailed. In section 3, the orthosis design and
the global control strategy based on a two mode system are
explained. In Section 4, the practical needs about frequency
estimation and phase-resetting problems when applying this
pattern to real orthoses are exposed and solved. In Section 5,
most relevant results are displayed in simulation. Those
results are available in more details in the enclosed video.
II. H UMAN GAIT MODELED BY PCPG
This section aims at describing the PCPG principles to
model gait as well as the way to extend the current study
of ankle orthosis to full prostheses.
with
�� =
�0
��
���(�0 )���−1 (− )
�0
�0
and
� (�) = �����ℎ (�) −
�
∑
(7)
Fig. 1: The PCPG is able to learn the frequency components of a
periodic signal as well as the various phases and magnitudes. The
main interest of PCPGs is the possibility to modify a learned pattern
in amplitude or frequency in a smooth way. This Figure is inspired
from [11].
1
PCPG output
Standard walk pattern
0.8
Normalized magnitude
A PCPG is able to learn a standard gait pattern. This
standard gait pattern was obtained by averaging about 50
gait cycles performed on a treadmill, determined and synchronized by a peak detection algorithm able to locate all
the relevant maxima in the kinematics recordings. Ideally, the
standard pattern could be derived from the subject himself
before his accident or, more probably, from a similar subject
in terms of his gait parameters such as age, height, etc [5].
As defined in [11], a PCPG is a kind of Fourier series decomposition and is composed of several adaptive oscillators.
This algorithm is governed by the following equation system:
⎧
�˙ � = �(� − ��2 )�� − �� �� + �� (�) + �� ���(�� − �� ) (1)
�˙ � = �(� − ��2 )�� + �� ��
(2)
��
(3)
⎨�˙ � = −�� (�)
��
�˙ � = ��� � (�)
(4)
˙
�
=
0
(5)
0
⎩�˙ � = ���(�� − ���(�� )���−1 (− �� ) − �� ), ∀� ∕= 0 (6)
��
0.6
0.4
0.2
0
−0.2
−0.4
−0.6
�� � �
(8)
�=0
As depicted in Figure 1, oscillators are coupled between each
other. The instantaneous phase of the fundamental oscillator
�0 is scaled at the frequency �� through �� and the phase
difference with the fundamental oscillator is given by �� . In
the standard approach, the coupling constant �� is chosen
constant over all oscillators. They are each composed of one
adaptive magnitude coefficient �� and one frequency parameter �� . � has a role of normalization of the learned pattern
1
with respect to �� = (�2� + ��2 ) 2 . The other parameters � and
� aim at accelerating the convergence while limiting stability
problems [11]. The �������� (�) signal resulting from the sum
of oscillator outputs is compared to the �����ℎ (�) gait pattern
target and the error value � (�) is computed. Throughout
the learning step consisting in integrating the differential
equations by a 4th order Runge-Kutta method with a fixed
step size, all the parameters of the PCPG are modified in
order to minimize � (�) (the typical learning time is less than
five minutes on a standard laptop). When this learning step
is finished, � (�) is close to zero and the system is generating
the right pattern as depicted in Figure 2.
Properties of PCPGs make them suitable for trajectory
generation in robotics and also for prosthesis applications. In
fact, the pattern learned by a PCPG can be easily controlled
in magnitude and in frequency thanks to a simple linear
767
768
769
Time (s)
770
771
Fig. 2: The PCPG is able to learn quasi-perfectly an average
normalized pattern of foot relative angle by means of 5 oscillators
determined by the frequency complexity of the signal.
change of the �
⃗ and �
⃗ vectors representing the ℜ� PCPG
states (� is the number of oscillators). This linearity leads
to a smooth change of the global system behavior. For
instance, if the �
⃗ vector is divided by two, the underlying
frequency of the standard temporal pattern is divided by
two. The same effect occurs for the �
⃗ vector, which governs
the pattern magnitude.
Finally, as proposed in [12], it is possible to couple several
PCPGs to model different joint angles. This is performed
thanks to equations of coupling between the fundamental
oscillators of each PCPG and by learning the phase difference:
⎧
2
⎨�˙ 0,� =�(� − �0,� )�0,� − �0,� �0,�
(9)
+ � ���(�0,�−1 − �0,� )
⎩˙
�0,� = ���(�0,�−1 − �0,� − �0,� )
(10)
where (0, �) denotes the first oscillator of the �th PCPG
(frequencies of joint angles are the same).
III. O RTHOSIS DESIGN AND CONTROL
This section first describes the orthosis hardware and
design. Then, the focus is on the gait cycle control strategies.
A. Orthosis hardware
As shown in Figure 3, the orthosis is made of several
components: two custom-fit plastic shells, two commercial
flexure joints, a linear actuator, a ball-link transmission,
a load cell to measure the actuator force, and two force
sensors installed in the orthosis sole, under the heel and
the toes. The plastic shells were designed using a 3D
scan of the right foot and leg of a healthy subject, adding
mounting surfaces for the actuator, the flexure joints, and the
mechanical transmission. The actuator includes a position
control unit based on a PID controller that can be driven by
an external analog signal in the range of 0 to 10 V.
However, this first prototype is quite cumbersome. One
of the problems that still represent an obstacle in the
development of active orthoses is the weight of the
commercial actuators. For this preliminary prototype, a
commercial actuator including the motor, the transmission,
and the control electronics was the first choice to facilitate
the construction in the shortest time. The weight of the
actuators found in the market that satisfied the mechanical
requirements for developing a complete gait cycle was
above 3.5 kg. In order to reduce the actuator weight,
the selection was done according to an estimate of the
root-mean-square torque during the swing phase and the
maximum velocity developed by the ankle in one gait
cycle, considering the maximum intrinsic ankle stiffness and
damping coefficients. The chosen actuator weight is about
1.6 kg and its maximum power is around 117 W, which
corresponds to a third of the peak power developed by a
healthy ankle [13]. In the current stage of the project, a
lightweight custom-fit actuator with passive energy-storage
elements is being developed, powerful enough for the stance
phase as well. Control strategies specific to an active stance
phase are explored in parallel. These developments will be
included in future prototypes.
The control and the PCPG algorithms reside in a
DsPIC 30F4013 microcontroller running at 120 MHz. Both
algorithms are calculated at each time step at a sampling
frequency of 500 Hz but the output of one or the other
is chosen according to the orthosis state, as detailed in
the next section. The differential equations of the PCPG
are solved by a simple explicit Euler integration method.
The microcontroller manages three analog (two sole force
sensors and one load cell) inputs for the signals coming from
the force sensors. The position command is generated using
Pulse Width Modulation (PWM) with a 9-mV resolution
after amplification. Initialization commands for the actuator
are generated using four digital outputs whereas the actuator
state (home sequence completion) is monitored using one
digital input.
A typical gait cycle for ground-level walking has a fundamental frequency around 1 Hz. To minimize the effects of
noise coming from the sensors and to smooth the command
signal, the analog inputs and the PWM output are filtered by
low-pass first-order filters with a cutoff frequency of 15 Hz,
which mainly determines the bandwidth. The microcontroller
architecture is shown in Figure 4.
ACTUATOR MOTOR
AND
ELECTRONIC UNIT
PLASTIC SHELL
(SHANK)
LINEAR
ACTUATOR
COMMERCIAL
FLEXURE JOINTS
LOAD CELL
PLASTIC SHELL
(FOOT)
LINK−BALL TRANSMISSION
Fig. 3: The orthosis prototype is made of several important components.
B. Gait cycle control strategies
In gait, there are two main events: the Heel Strike (HS)
and the Toe Off (TO) for each foot, which correspond to
the time of the initial contact of the heel with the ground
and the time of the last contact of the toes respectively.
There are also two different phases: the stance phase, i.e.
between the HS and the TO, and the swing phase, i.e.
between the TO and the HS when the foot is in the air. As
the objective of the orthosis in this preliminary stage is to
help people with foot drop problems, the orthosis comprises
two different control modes, one for the stance phase when
the subject entirely drives the orthosis and another one for
the swing phase when the PCPG output governs the system.
The first mode is active during the stance phase, allowing
the free motion of the foot around an equilibrium point
(approximately at a foot angle of 90∘ with respect to the
tibia) but, at the same time, providing a certain level of
stability through a virtual stiffness element. This part of the
control algorithm is based on the following expression
�˙ = � (� − � (� − �� ))
(11)
where �˙ is the time derivative of the actuator position,
denoted by �; � = 6.6 mm
Ns is the proportional gain; � is the
N
actuator force, measured by the load cell; � = 3.8 mm
is the
desired virtual stiffness; and �� = 39.1 mm is the desired
equilibrium position. The actuator possesses an internal
controller that regulates the rod position taking an external
DIGITAL OUTPUTS
INSTRUMENTATION AMPLIFIER
30F4013
(120 MHz, 16 −BIT)
PWM OUTPUT
DsPIC
LOW−PASS FILTER (15 Hz)
FOREFOOT
SENSOR
DIGITAL INPUTS
ANALOG INPUTS
HEEL SENSOR
INSTRUMENTATION AMPLIFIER
LOAD CELL
LOW−PASS FILTER ARRAY (15 Hz)
ACTUATOR STATE
LINEAR
ACTUATOR
INITIALIZATION COMMANDS
Fig. 4: The block diagram shows the microcontroller architecture.
analog signal as a reference. In our scheme, � represents
this external signal. The internal controller has a relatively
fast response considering the time scale in which a normal
stride develops so the displacement command � is assumed
to be equivalent to the actual actuator displacement. Because
of the stable linear system structure, if � remains bounded,
the stability at the output � is insured. A stability analysis
of the whole system and tests using the foot sensors are
part of future work.
The second mode is associated to the swing phase and
is intended to help the patient to achieve enough foot
clearance to initiate the next gait cycle. As a preliminary
approximation, this part of the algorithm will mainly consist
in a trajectory tracking scheme to follow the PCPG position
pattern similar to that developed by a healthy foot during
the swing phase. This tracking is performed by the internal
controller.
. For
estimated angular frequency is given by ���� = �2�
���
easier frequency control purpose, the Normalized Frequency
Control (NFC) parameter �� is obtained by �� = �����
0
where �0 is the fundamental angular frequency of the
learned standard pattern.
By experiments, it was noticed that the observed frequency
at the output of the PCPG ���� and the command frequency
���� were not equal, probably due to the integration method
(Euler method is known to modify the period of integrated
signals). A linear regression was sufficient to perfectly model
this frequency shift:
���� = 1.0294 ∗ ���� − 0.04
(12)
This easily leads to:
�� =
(���� + 0.08 ∗ ��)
(1.0294(�0 ))
(13)
B. Phase-resetting
IV. P RACTICAL NEEDS
This section gives more details on the needs for frequency
adaptation of the PCPG output and phase-resetting in order
to deal with practical problems. Firstly, to adapt the gait
pattern frequency at each step, the frequency estimation
method is based on gait events and a simple method to
correct the PCPG frequency shift is proposed. Secondly,
it introduces two approaches of phase-resetting: a hard
one and a novel scheme to achieve a smooth and robust
adaptation.
A. Frequency adaptation
Given that the only available information is the TO and
HS events, they have to be used to determine the gait
period. In this paper, this period estimation ���� is obtained
by the time between two successive HS of the same
foot. To consider when the subject is stopping walking, if
the next HS does not appear after a predetermined time
���� , the orthosis switches to the follower mode. Because
the PCPG has to be controlled in angular frequency, the
Obviously, at constant speed, gait cycles are not perfectly
identical [14]. This fact and numerous perturbations can
provoke phase mismatch between the perfectly periodic
PCPG output and the real gait pattern in addition to change
in frequency. If this mismatch is too important, the subject
has to compensate for it leading to a non-natural gait. The
aim of this phase-resetting is to pave the way to allow the
orthosis to adapt to the patient as quickly and smoothly as
possible aiming at increasing the subject comfort.
The phase-resetting consists in resynchronizing the PCPG
state according to special events. Therefore, the PCPG will
be phase reset on the HS to allow the system to recover the
correct phase in a smooth way at the time of the TO. Two
approaches are available: a hard and a soft phase-resetting.
The hard phase-resetting relies on a direct modification
of the integrated values: in each oscillator �, �� and �� are
put to standard values corresponding to the HS event. The
main advantage of this approach is the quick phase-locking
whereas the disadvantages are (1) a more sensitive reaction
to noise in the frequency estimation due to small variations
1
0.1
0.9
−0.2
PCPG output
Heel Strike
NFC
−0.5
−0.8
40
45
0.8
50
Time (s)
55
0.7
1.2
0.7
1.1
0.4
1
0.1
0.9
−0.2
−0.5
−0.8
40
PCPG output
Heel Strike
NFC
45
0.8
50
Time (s)
55
0.7
Normalized frequency control parameter
1.1
0.4
1.3
1
Normalized PCPG output
Normalized PCPG output
1.2
0.7
Normalized frequency control parameter
1.3
1
Fig. 5: On the left: without enhancements, the soft phase-resetting leads to an important and long transient. On the right: this problem is
strongly mitigated.
in gait cycles at constant speeds or in the measurement itself
and (2) important modification of the actuator state leading
to a delay of recovery because of the low-pass filter. In the
case of a foot lifter orthosis, during the stance phase, the
actuator is not commanding the system and thus, the latter
disadvantage is mitigated. However, it is a real problem in
the case of a complete prosthesis.
In the soft phase-resetting, the original PCPG algorithm
was slightly modified. To control the phase of the first
PCPG oscillator, a coupling with a reference oscillator at
instantaneous phase �0,� was established. This allows to
modify the phase difference �0,� between the reference
oscillator and the first oscillator of the PCPG. Formally, the
reference oscillator is as follows:
2
)�0,� − �0,� �0,� + � ���(�0,� )
�˙ 0,� = �(� − �0,�
(14)
whereas the coupling with the PCPG (subscripted by �) is
shown in:
2
�˙ 0,� = �(�−�0,�
)�0,� −�0,� �0,� +� ���(�0,� �−�0,� ) (15)
�
where � = �0,�
. The coupling with the other oscillators of
0,�
the PCPG is identical to the previous description. Because
the phase of higher order oscillators had more difficulties to
follow a phase change in experiments, coupling constant was
��,�
. Figure 5 shows how this modification
defined as �� = � �0,�
can produce a smooth and robust kinematics output when a
phase reset is applied. In our experiments, we chose � = 1.
V. R ESULTS
In this section, results about the system response to two
realistic perturbations for both phase-resetting techniques are
presented. These were obtained using the microcontroller
connected to the orthosis (the PCPG output shows the
command of the orthosis, not the actual position of it). Gait
events were generated by simulation. First, a steep speed
change is considered. Then, a speed change according to a
chirp function is described in order to expose the behavior
of each technique to a highly changing environment.
To model a rapid change of frequency, i.e. of the
subject’s speed, the heel strike period is suddenly modified.
To be coherent with practical applications, speed is
instantaneously decreased by 1 km/h from the speed of
4 km/h (from �� = 1.13 to �� = 1). As depicted in
Figure 6, the hard phase-resetting is recovering the correct
phase quasi-instantaneously at the price of a non-continuous
modification (with a small transient due to the low-pass
filtering when connected to the orthosis) whereas the soft
phase-resetting takes much more time for recovering (less
than two gait cycles) with a very smooth transition. Similar
results are obtained when the speed is suddenly increased.
To show the behavior of both techniques in quasicontinuous speed adaptation, a chirp function was used to
decrease the normalized frequency control parameter �� .
This parameter was decreased from 1.3 to 0.7 by 0.1 step
every two heel strikes of the same foot, which corresponds to
a deceleration from of 6 km/h to 1.5 km/h in 12 gait cycles
for the studied subject. As depicted in Figure 7, at first,
when the frequency is changed, the system can not detect
this information and is not adapting leading to a certain
phase delay depending on the frequency difference. Then,
at the following heel strike, the system detects the correct
phase and frequency. Regarding the hard phase-resetting,
the phase adaptation is done quasi-immediately as in the
steep speed change whereas the soft resetting method takes
more time and does not converge quickly enough to totally
recover the phase when the next modification occurs. The
same conclusion about the discontinuities can be reported. In
a smooth approach, our frequency estimation scheme implies
an intrinsic delay, namely the system can not predict the
actual speed before the HS. Enhancements could be brought
by using for instance additional gait events or sensors.
1
0.1
−0.2
0.9
−0.5
0.8
−0.8
9
10
11
12
13
Time (s)
14
15
0.7
16
0.7
1.1
0.4
1
0.1
−0.2
0.9
−0.5
0.8
−0.8
9
10
11
12
13
Time (s)
14
15
0.7
16
Normalized frequency control parameter
1.1
0.4
1.3
PCPG output
Heel Strike
1.2
NFC
1
Normalized PCPG output
Normalized PCPG output
0.7
Normalized frequency control parameter
1.3
PCPG output
Heel Strike
1.2
NFC
1
1.1
0.4
1
0.1
−0.2
0.9
−0.5
0.8
−0.8
5
10
Time (s) 15
20
0.7
0.7
1.1
0.4
1
0.1
−0.2
0.9
−0.5
0.8
−0.8
5
10
Time (s) 15
20
0.7
Normalized frequency control parameter
Normalized PCPG output
0.7
1.3
PCPG output
Heel Strike
1.2
NFC
1
Normalized PCPG output
1.3
PCPG output
Heel Strike
1.2
NFC
1
Normalized frequency control parameter
Fig. 6: On the left: the hard phase-resetting is able to quasi-immediately recover the correct phase at the price of discontinuities shown
by the arrow. This clearly leads to be more sensitive to frequency estimation noise. On the right: the soft phase-resetting is recovering
the phase quite slowly but in a perfectly continuous way. Indeed, the first arrow on the left is showing the correct place of the HS event.
The next ones indicate that there is a decreasing phase offset.
Fig. 7: On the left: the hard phase-resetting is working as in the steep speed change. On the right: the soft phase-resetting does not totally
recover the right phase when a speed change occurs as indicated by the arrows. However, the phase delay is quite small.
VI. C ONCLUSION AND FUTURE WORK
A. Conclusion
In this paper, the fundamentals and principles of an
original foot lifter orthosis integrating a human gait model
are exposed. The system is composed of three main parts:
a PCPG-based model of human locomotion, a sole sensorbased gait control switching strategy and a heel strike-based
phase-resetting approach.
Modeling human gait by PCPG is greatly facilitated by
its controllability in frequency and magnitude. Moreover,
smooth transitions produced by this model are highly
relevant for this purpose.
A twofold gait control strategy was developed. Sole sensors
provide information about gait events. When the stance
phase is detected, the follower mode allowing the patient
to entirely govern the orthosis is activated. Otherwise, the
PCPG human model is fed in the orthosis, which aims
at mimicking as closely as possible the gait pattern and
increasing the patient’s comfort.
Two phase-resetting methods are detailed and their advantages and disadvantages are discussed. A hard version
consists in directly modifying the values of the PCPG at
the heel strike event, which could lead to discontinuities in
the pattern. This problem can be mitigated in a foot lifter
orthosis because of the specific control strategy (during the
stance phase, after a HS, the orthosis is in the follower
mode) but has to be improved in full prostheses. Therefore,
a second and original soft phase-resetting scheme based
on a reference oscillator and a harmonic-adaptive coupling
constant is described leading to a smooth PCPG output
transition. This scheme is obviously more robust to slight
frequency estimation variations at constant speed. However,
when based on heel strike events, it is at this time not
sufficiently rapid to recover the phase.
B. Future work
Future work will be dedicated to increasing the speed of
phase recovery in the soft phase recovering while keeping
the smooth aspect. In addition to parameter optimization,
this could be done by using other gait events or sensors in
both frequency estimation and phase-resetting. An alternative
could be to combine the hard and soft phase-resettings
to increase reactivity while remaining quite smooth. For
instance, heel strikes of the injured foot could be used with
hard phase-resetting, while toe off of the injured foot could
be used with soft phase-resetting to slowly recover the phase
and facilitate the hard phase-resetting work. This procedure
would avoid steep magnitude modification just before the
injured foot leaves the ground.
From experimental data, the evaluation of metrics such as the
settling time, i.e. the time the system needs to recover the
phase given a certain error band, could be interesting to precisely characterize the recovery speed of soft phase-resetting.
When combining both phase-resettings, the determination of
the hard phase reset step distribution with and without soft
phase-resetting in realistic application will make it possible
to judge the relevancy of this combination to reduce the
magnitude of the hard phase-resetting step.
Pattern magnitude is known to vary according to the speed.
Such magnitude adaptation could be envisaged aiming at
further increasing the patient’s comfort [15]. Similarly, the
frequency- and magnitude-adjusted gait pattern is also known
to slightly differ as a function of speed. As proposed in [15],
this could be solved by combining two PCPGs whose learned
frequencies are quite different and correspond to different
ranges of gait speeds.
Regarding the orthosis itself, energy-storage elements powerful enough for the stance phase will be implemented. Control
strategies specific to an active stance phase are explored in
parallel. Also, a main point of interest will be the inclusion of
a lightweight custom-fit actuator with passive energy storage
elements.
Finally, the feedback from the patients will make it possible
to correctly design the final orthosis.
ACKNOWLEDGMENTS
M. Duvinage is a FNRS (Fonds National de la Recherche
Scientifique) Research Fellow.
The authors thank Kevin Nis, who implemented the PCPG
algorithm on the microcontroller and produced the video.
The authors want to express their gratitude for the financial
support provided by Fonds Européen de Développement
Régional (FEDER) and the Région Wallonne.
This paper presents research results of the Belgian Network
DYSCO (Dynamical Systems, Control, and Optimization),
funded by the Interuniversity Attraction Poles Programme,
initiated by the Belgian State, Science Policy Office. The
scientific responsibility rests with its author(s).
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